Ultrasound imaging has been developed for image-guided radiotherapy for tumor tracking, and the flexible array transducer is a promising tool for this task. It can reduce the user dependence and anatomical changes caused by the traditional ultrasound transducer. However, due to its flexible geometry, the conventional delay-and-sum (DAS) beamformer may apply incorrect time delay to the radio-frequency (RF) data and produce B-mode images with considerable defocusing and distortion. To address this problem, we propose a novel end-to-end deep learning approach that may alternate the conventional DAS beamformer when the transducer geometry is unknown. Different deep neural networks (DNNs) were designed to learn the proper time delays for each channel, and they were expected to reconstruct the undistorted high-quality B-mode images directly from RF channel data. We compared the DNN results to the standard DAS beamformed results using simulation and flexible array transducer scan data. With the proposed DNN approach, the averaged full-width-at-half-maximum (FWHM) of point scatters is 1.80 mm and 1.31 mm lower in simulation and scan results, respectively; the contrast-to-noise ratio (CNR) of the anechoic cyst in simulation and phantom scan is improved by 0.79 dB and 1.69 dB, respectively; and the aspect ratios of all the cysts are closer to 1. The evaluation results show that the proposed approach can effectively reduce the distortion and improve the lateral resolution and contrast of the reconstructed B-mode images.

Despite great promise, adoptive cell therapy (ACT) continues to fail at treating a majority of cancers, especially solid tumors. To inform development and expedite the translation of more potent cellular immunotherapies, advanced immunoimaging tools are needed to better understand the in vivo requirements for generating a robust immune response. Even methods to evaluate the delivery, location, and status of transferred T cells at the tumor target are lacking. Therefore, a real-time, safe, noninvasive, longitudinal imaging method is critically needed to 1) monitor adoptive T cell location and status and 2) assess treatment progression and response through imaging biomarkers. Here, we developed a combined ultrasound (US) and photoacoustic (PA) imaging approach to enable T cell tracking following adoptive transfer for cancer immunotherapy. Our approach leverages highly photostable gold nanorods and cell surface engineering to tag the T cells without impacting effector functions, as well as generate PA contrast for imaging post-transfer. Our in vivo US/PA imaging approach detected nanoparticle-labeled T cell accumulation at the tumor, visualized changes in tumor volume, and conveyed accompanying changes in blood biomarkers. US/PA data also showed different trends according to a positive or negative antitumor response to T cell therapy over 7 days. Results highlight the potential of the approach and motivate future development to expand the platform for advanced, theranostic immunoimaging.

Pancreatic cancer with less than 10% 3-year survival rate is one of deadliest cancer types and greatly benefits from enhanced radiotherapy. Organ motion monitoring helps spare the normal tissue from high radiation and, in turn, enables the dose escalation to the target that has been shown to improve the effectiveness of RT by doubling and tripling post-RT survival rate. The flexible array transducer is a novel and promising solution to address the limitation of conventional US probes. We proposed a novel shape estimation for flexible array transducer using two sequential algorithms: (i) an optical tracking-based system that uses the optical markers coordinates attached to the probe at specific positions to estimate the array shape in real-time and (ii) a fully automatic shape optimization algorithm that automatically searches for the optimal array shape that results in the highest quality reconstructed image. We conducted phantom and in vivo experiments to evaluate the estimated array shapes and the accuracy of reconstructed US images. The proposed method reconstructed US images with low full-width-at-half-maximum (FWHM) of the point scatters, correct aspect ratio of the cyst, and high-matching score with the ground truth. Our results demonstrated that the proposed methods reconstruct high-quality ultrasound images with significantly less defocusing and distortion compared with those without any correction. Specifically, the automatic optimization method reduced the array shape estimation error to less than half-wavelength of transmitted wave, resulting in a high-quality reconstructed image.

Microcirculation facilitates the exchange of gases, nutrients, and waste products between blood and tissues and is critical for maintaining systemic tissue health. In this study, we introduce photoacoustic-strain (PAS) imaging, a non-invasive method for assessing tissue microcirculation. By combining externally applied deformation while performing ultrasound/photoacoustic imaging, PAS enables real-time monitoring of blood volume changes as blood is displaced from the tissue under pressure and subsequently refilled upon pressure release. Through a series of post-processing steps, spatially resolved maps of blood displacement and reperfusion rates are reconstructed, providing insights into microcirculatory dynamics. Using both in silico and in vivo experiments, PAS imaging was developed and validated, demonstrating its capability to detect and differentiate changes in microcirculation under various conditions, including vascular occlusion and tissue at different temperatures, with high sensitivity and strong robustness. These findings underscore the potential of PAS imaging as a non-invasive tool for understanding and diagnosing diseases associated with microcirculatory function.

In the production process of fabric, defect detection plays an important role in the control of product quality. Consider that traditional manual fabric defect detection method are time-consuming and inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can infer the attention map from the intermediate feature map and multiply the attention map to adaptively refine the feature. This method improve the performance of classification and detection without increase the computation-consuming. The experiment results show that Faster RCNN with attention module can efficient improve the classification accuracy.

Photoacoustic (PA) imaging has potential for various preclinical and clinical applications such as cancer diagnosis, treatment monitoring, and assessment of vascularity and hemodynamics.^1,2 This noninvasive modality uses nanosecond pulsed laser illumination of tissue, followed by absorption of light energy, thermoelastic expansion, and the emission of a broadband acoustic wave. This acoustic signal can then be detected by conventional ultrasound (US) hardware to produce an image with molecular contrast. Quantitative PA imaging uses multiwavelength PA imaging together with the known absorption spectra of various chromophores to estimate their concentration in tissue.³ These chromophores can be either endogenous such as hemoglobin,⁴ melanin,⁵ collagen, or exogenous such as gold nanoparticles or dyes,^6,7 with applications such as early detection of lymph node metastasis, longitudinal monitoring of hypoxia in solid tumors, or tracking of nanoparticle-labeled cells in the tissue.^{8 – 11} These applications require accurate quantitative PA imaging to avoid incorrect classification of disease states or inaccurate counts of labeled cells in vivo. However, quantitative PA imaging relies on knowledge of tissue optical properties as well as the local fluence at every point in the image for accurate estimation of the local PA signal. Unfortunately, direct measurements of local fluence in the tissue require invasive probes,¹² and estimation of local fluence remains an ill-posed inverse problem with many proposed solutions.^13,14